In many symbol sets, there are many ways to communicate the same information. For example, a particular location within a codex might be expressed as “page X, line Y,” “p. X, l. Y,” or “pg. X; li, Y.” Yet, as different as these are, they all mean the same thing: a particular location of certain text within a codex.
A human being generally has little difficulty in coping with having so many different ways to say the same thing. To a human, this task is natural. It is so deeply embedded in his functioning that, if asked exactly how he does it, he will most likely be unable to offer a clear answer.
This lack of consistency in expression, however, poses difficulty for information-mining robots. As an information-mining robot automatically reads symbols in search of information, it inevitably encounters different ways of communicating the same information. Such a robot must be taught to understand, for example, that “page X, line Y” and “p. X, l. Y” mean the same thing.
An obvious way to solve this problem is the brute force approach. For example, one can simply tell the robot about each possible way of expressing a location in a symbol set. Armed with such a list, a robot that encounters an unknown pattern of symbols can compare it with each such pre-programmed expression to see if it fits.
This approach has certain disadvantages. First of all, the process becomes more time-consuming as the list of alternatives becomes longer. Secondly, a great deal of programming is required.